• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.2
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• pp.47-52
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• 2016

The used digital images in the smart device and small displayer has been a downscaled image. In this paper, the detection of the downscaling image forgery is proposed using the feature vector according to the pixel value's gradients. In the proposed algorithm, AR (Autoregressive) coefficients are computed from pixel value's gradients of the image. These coefficients as the feature vectors are used in the learning of a SVM (Support Vector Machine) classification for the downscaling image forgery detector. On the performance of the proposed algorithm, it is excellent at the downscaling 90% image forgery compare to MFR (Median Filter Residual) scheme that had the same 10-Dim. feature vectors and 686-Dim. SPAM (Subtractive Pixel Adjacency Matrix) scheme. In averaging filtering ($3{\times}3$) and median filtering ($3{\times}3$) images, it has a higher detection ratio. Especially, the measured performances of all items in averaging and median filtering ($3{\times}3$), AUC (Area Under Curve) by the sensitivity and 1-specificity is approached to 1. Thus, it is confirmed that the grade evaluation of the proposed algorithm is 'Excellent (A)'.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.5
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• pp.65-73
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• 2017

In this paper, we propose a stereo matching method to improve the image quality at the hole and the disparity discontinuity regions. The stereo matching method extracts disparity map finding corresponding points between stereo image pair. However conventional stereo matching methods have a problem about the tradeoff between accuracy and precision with respect to the length of the baseline of the stereo image pair. In addition, there are hole and disparity discontinuity regions which are caused by textureless regions and occlusion regions of the stereo image pair. The proposed method extracts initial disparity map improved at disparity discontinuity and miss-matched regions using modified AD-Census-Gradient method and adaptive weighted cost aggregation. And then we conduct the disparity map refinement to improve at miss-matched regions, while also improving the accuracy of the image. Experimental results demonstrate that the proposed method produces high-quality disparity maps by successfully improving miss-matching regions and accuracy while maintaining matching performance compared to existing methods which produce disparity maps with high matching performance. And the matching performance is increased about 3.22(%) compared to latest stereo matching methods in case of test images which have high error ratio.

• Investigative Magnetic Resonance Imaging
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• v.23 no.3
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• pp.179-201
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• 2019

Portable low-cost magnetic resonance imaging (MRI) systems have the potential to enable "point-of-care" and timely MRI diagnosis, and to make this imaging modality available to routine scans and to people in underdeveloped countries and areas. With simplicity, no maintenance, no power consumption, and low cost, permanent magnets/magnet arrays/magnet assemblies are attractive to be used as a source of static magnetic field to realize the portability and to lower the cost for an MRI scanner. However, when taking the canonical Fourier imaging approach and using linear gradient fields, homogeneous fields are required in a scanner, resulting in the facts that either a bulky magnet/magnet array is needed, or the imaging volume is too small to image an organ if the magnet/magnet array is scaled down to a portable size. Recently, with the progress on image reconstruction based on non-linear gradient field, static field patterns without spatial linearity can be used as spatial encoding magnetic fields (SEMs) to encode MRI signals for imaging. As a result, the requirements for the homogeneity of the static field can be relaxed, which allows permanent magnets/magnet arrays with reduced sizes, reduced weight to image a bigger volume covering organs such as a head. It offers opportunities of constructing a truly portable low-cost MRI scanner. For this exciting potential application, permanent magnets/magnet arrays have attracted increased attention recently. A magnet/magnet array is strongly associated with the imaging volume of an MRI scanner, image reconstruction methods, and RF excitation and RF coils, etc. through field patterns and field homogeneity. This paper offers a review of permanent magnets and magnet arrays of different kinds, especially those that can be used for spatial encoding towards the development of a portable and low-cost MRI system. It is aimed to familiarize the readers with relevant knowledge, literature, and the latest updates of the development on permanent magnets and magnet arrays for MRI. Perspectives on and challenges of using a permanent magnet/magnet array to supply a patterned static magnetic field, which does not have spatial linearity nor high field homogeneity, for image reconstruction in a portable setup are discussed.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1245-1254
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• 2023

Significant research has been conducted on the W-band synthetic aperture radar (SAR) system that utilizes the 77 GHz frequency modulation continuous wave (FMCW) radar. To reconstruct the high-resolution W-band SAR image, it is necessary to transform the point cloud acquired from the stereo cameras or the LiDAR in the direction of 6 degrees of freedom (DOF) and apply them to the SAR signal processing. However, there are difficulties in matching images due to the different geometric structures of images acquired from different sensors. In this study, we present the method to extract an optimized depth map by obtaining 6 DOF of the point cloud using a gradient descent method based on the entropy of the SAR image. An experiment was conducted to reconstruct a tree, which is a major road environment object, using the constructed W-band SAR system. The SAR image, reconstructed using the entropy-based gradient descent method, showed a decrease of 53.2828 in mean square error and an increase of 0.5529 in the structural similarity index, compared to SAR images reconstructed from radar coordinates.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.137-140
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• 1996

Finding line segments in an intensity image has been one of the most fundamental issues in computer vision. In complex scenes, it is hard to detect the locations of point features. Line features are more robust in providing greater positional accuracy. In this paper we present a robust "line features extraction" algorithm which extracts line feature in a single pass without using any assumptions and constraints. Our algorithm consists of five steps: (1) edge scanning, (2) edge normalization, (3) line-blob extraction, (4) line-feature computation, and (5) line linking. By using edge scanning, the computational complexity due to too many edge pixels is drastically reduced. Edge normalization improves the local quantization error induced from the gradient space partitioning and minimizes perturbations on edge orientation. We also analyze the effects of edge processing, and the least squares-based method and the principal axis-based method on the computation of line orientation. We show its efficiency with some real images.al images.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.167-170
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• 1995

An frequency modulated (FM) DANTE pulse sequence generates a quadratic phase toward the transverse of image by an FM RF pulse. In the image of a serious susceptibility effect, the phase due to the difference of the susceptibility in the pixel occurs susceptibility error which arise signal loss. But the signal loss due to the susceptibility effect in the pixel is reduced when the quadratic phase adds in the pixel. In this paper, we have generated a quadratic function toward the transverse (X-Y) using FM DANTE sequence and the susceptibility effect is reduced in the gradient echo (GE) imaging. Computer simulation and experimental results is obtained by using a whole-body KAIS 2.0T NMR system.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.7
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• pp.132-139
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• 1989

Optical flow is important not only for determining velocity and trajectory of the object but also for image segmentation and 3D information. The gradient-based method is mostly used to compute optical flow form image sequences, but it accomanies smoothing effect of velocity vectors. In this paper, an enhanced algorithm for computing optical flow using scalar edge to restrict is also applied to reduce errors both around motion boundary and in the occlusion region.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.11
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• pp.2776-2788
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• 1996

A new feature-based motion parameter estimation for arbitrary-shaped regions is proposed. Existing motion parameter estimation algorithms such as gradient-based algorithm require iterations that are very sensitive to initial values and which often converge to a local minimum. In this paper, the motion parameters of an object are obtained by solving a set of linear equations derived by the motion of salient feature points of the object. In order to estimate the displacement of the feature points, a new process called the "bi-directional correspondence scheme" is proposed to ensure the robjstness of correspondence. The proposed correspondence scheme iteratively selects the feature points and their corresponding points until unique one-to-one correspondence is established. Furthermore, initially obtained motion paramerters are refined using an iterative method to give a better performance. The proposed algorithm can be used for motion estimationin object-based image coder, and the experimental resuls show that the proposed method outperforms existing schemes schemes in estimating motion parameters of objects in image sequences.sequences.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4534-4555
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• 2015

We propose a robust object cataloging method using multiple locally distinct heterogeneous features for aiding image retrieval. Due to challenges such as variations in object size, orientation, illumination etc. object recognition is extraordinarily challenging problem. In these circumstances, we adapt local interest point detection method which locates prototypical local components in object imageries. In each local component, we exploit heterogeneous features such as gradient-weighted orientation histogram, sum of wavelet responses, histograms using different color spaces etc. and combine these features together to describe each component divergently. A global signature is formed by adapting the concept of bag of feature model which counts frequencies of its local components with respect to words in a dictionary. The proposed method demonstrates its excellence in classifying objects in various complex backgrounds. Our proposed local feature shows classification accuracy of 98% while SURF,SIFT, BRISK and FREAK get 81%, 88%, 84% and 87% respectively.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2222-2224
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• 1998

This paper presents a 3D feature extraction method using rough set theory. Using the stereo cameras, we obtain the raw images and then perform several processes including gradient computation and image matching process. Decision rule constructed via rough set theory determines whether a ceratin point in the image is 3D edge or not. We propose a method finding rules for 3D edge extraction using rough set.